A visibility improvement technique for fog images suitable for real-time application

Cameras used in outdoor scenes require high visibility performance under various environmental conditions. We present a visibility improvement technique which can improve the visibility of images captured in bad weather such as fog and haze, and also applicable to real-time processing in surveillance cameras and vehicle cameras. Our algorithm enhances contrast pixel by pixel according to the brightness and sharpness of neighboring pixels. In order to reduce computational costs, we preliminarily specify the adaptive functions which determine contrast gain from brightness and sharpness of neighboring pixels. We optimize these functions using the sets of fog images and examine how well they can predict the fog-degraded area using both qualitative and quantitative assessment. We demonstrate that our method can prevent excessive correction to the area without fog to suppress noise amplification in sky or shadow region, while applying powerful correction to the fog-degraded area. In comparison with other real-time oriented methods, our method can reproduce clear-day visibility while preserving gradation in shadows and highlights and also preserving naturalness of the original image. Our algorithm with low computational costs can be compactly implemented on hardware and thus applicable to wide-range of video equipments for the purpose of visibility improvement in surveillance cameras, vehicle cameras, and displays.